Corrigendum to The time has come: Bayesian methods for data analysis in the organizational sciences (Organizational Research Methods, 15, 4, (722-752), 10.1177/1094428112457829);The Time Has Come: Bayesian Methods for Data Analysis in the Organizational Sciences

Organizational Research Methods

Volumn 15, Issue 4, 2012, Pages 722-752

  Kruschke, John K ^a   Aguinis, Herman ^b   Joo, Harry ^b  

^a INDIANA UNIVERSITY   (United States)

^b INDIANA UNIVERSITY   (United States)

Author keywords

bootstrapping); computer simulation procedures (e.g.; Monte Carlo; multilevel research; quantitative research

Indexed keywords

EID: 84873827979     PISSN: 10944281     EISSN: 15527425     Source Type: Journal    
DOI: 10.1177/1094428113514358     Document Type: Erratum

References (79)

1. Aguinis H.,Beaty J. C.,Boik R. J.,Pierce C. A.Effect size and power in assessing moderating effects of categorical variables using multiple regression: A 30-year review.Journal of Applied Psychology. 2005;90:94-107.
2. Aguinis H.,Culpepper S. A.,Pierce C. A.Revival of test bias research in preemployment testing.Journal of Applied Psychology. 2010;95:648-680.
3. Aguinis H.,Harden E. E.Research methodology in strategy and management. Bergh D. D.Ketchen D. J., ed. Howard House, England: Emerald Group Publishing; 2009:111-120.
4. Aguinis H.,Pierce C. A.,Bosco F. A.,Muslin I. S.First decade of Organizational Research Methods: Trends in design, measurement, and data-analysis topics.Organizational Research Methods. 2009;12:69-112.
5. Aguinis H.,Werner S.,Abbott J. L.,Angert C.,Park J. H.,Kohlhausen D.Customer-centric science: Reporting research results with rigor, relevance, and practical impact in mind.Organizational Research Methods. 2010;13:515-539.
6. Allenby G. M.,Bakken D. G.,Rossi P. E.The hierarchical Bayesian revolution: How Bayesian methods have changed the face of marketing research.Marketing Research. 2004;16:20-25.
7. Anscombe F.Fixed sample size analysis of sequential observations.Biometrics. 1954;10:89-100.
8. Bayes T.,Price R.An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F.R.S. Communicated by Mr. Price, in a letter to John Canton, A.M.F.R.S.Philosophical Transactions. 1763;53:370-418.
9. Beaumont M. A.,Rannala B.The Bayesian revolution in genetics.Nature Reviews Genetics. 2004;5:251-261.
10. Berger J. O.,Berry D. A.Statistical analysis and the illusion of objectivity.American Scientist. 1988;76:159-165.
11. Berry D. A.Bayesian clinical trials.Nature Reviews: Drug Discovery. 2006;5:27-36.
12. Brannick M. T.Implications of empirical Bayes meta-analysis for test validation.Journal of Applied Psychology. 2001;86:468-480.
13. Brooks S. P.Bayesian computation: A statistical revolution.Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences. 2003;361:2681-2697.
14. Casciaro T.,Lobo M.When competence is irrelevant: The role of interpersonal affect in task-related ties.Administrative Science Quarterly. 2008;53:655-684.
15. Cashen L. H.,Geiger S. W.Statistical power and the testing of null hypotheses: A review of contemporary management research and recommendations for future studies.Organizational Research Methods. 2004;7:151-167.
16. Cohen J.The earth is round (p < 0.05).American Psychologist. 1994;49:997-1003.
17. Cornfield J.A Bayesian test of some classical hypotheses, with applications to sequential clinical trials.Journal of the American Statistical Association. 1966;61:577-594.
18. Cortina J. M.,Folger R. G.When is it acceptable to accept a null hypothesis: No way, Jose?.Organizational Research Methods. 1998;1:334-350.
19. Cortina J. M.,Landis R. S.Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences. Lance C. E.Vandenberg R. J., ed. New York, NY: Routledge; 2009:287-308.
20. Cortina J. M.,Landis R. S.The earth is not round (p =.00).Organizational Research Methods. 2011;14:332-349.
21. Culpepper S. A.,Aguinis H.R is for revolution: A cutting-edge, free, open source statistical package.Organizational Research Methods. 2011;14:735-740.
22. Cumming G.Inference by eye: Pictures of confidence intervals and thinking about levels of confidence.Teaching Statistics. 2007;29:89-93.
23. Cumming G.,Fidler F.Confidence intervals: Better answers to better questions.Zeitschrift fur Psychologie. 2009;217:15-26.
24. Dienes Z.Bayesian versus orthodox statistics: Which side are you on?.Perspectives on Psychological Science. 2011;6:274-290.
25. Doyle A. C.The sign of four. London, England: Spencer Blackett; 1890:.
26. Edwards J. R.,Berry J. W.The presence of something or the absence of nothing: Increasing theoretical precision in management research.Organizational Research Methods. 2010;13:668-689.
27. Gelman A.Objections to Bayesian statistics.Bayesian Analysis. 2008;3:445-450.
28. Gelman A.,Hill J.Data analysis using regression and multilevel/hierarchical models. New York, NY: Cambridge University Press; 2007:.
29. Gelman A.,Hill J.,Yajima M.Why we (usually) don't have to worry about multiple comparisons. New York: Department of Statistics, Columbia University; 2009:.
30. Gerard P. D.,Smith D. R.,Weerakkody G.Limits of retrospective power analysis.Journal of Wildlife Management. 1998;62:801-807.
31. Gray P. H.,Cooper W. H.Pursuing failure.Organizational Research Methods. 2010;13:620-643.
32. Gregory P. C.A Bayesian revolution in spectral analysis. AIP conference proceedings; 2001; 2001. 557.
33. Hays W. L.Statistics. 1994). Statistics (5th ed.). Fort Worth, TX: Harcourt Brace.Fort Worth, TX: Harcourt Brace; 1994:.
34. Hirst G.,Van Knippenberg D.,Zhou J.A cross-level perspective on employee creativity: Goal orientation, team learning behavior, and individual creativity.Academy of Management Journal. 2009;52:280-293.
35. Huang J. L.,Ryan A. M.Beyond personality traits: A study of personality states and situational contingencies in customer service jobs.Personnel Psychology. 2011;64:451-488.
36. Jackman S.Bayesian analysis for the social sciences. West Sussex, England: Wiley; 2009:.
37. King G.How not to lie with statistics: Avoiding common mistakes in quantitative political science.American Journal of Political Science. 1986;30:666-687.
38. Kline R. B.Beyond significance testing: Reforming data analysis methods in behavioral research. Washington, DC: American Psychological Association; 2004:.
39. Kruschke J. K.Bayesian data analysis.Wiley Interdisciplinary Reviews: Cognitive Science. 2010a;1:658-676.
40. Kruschke J. K.What to believe: Bayesian methods for data analysis.Trends in Cognitive Sciences. 2010b;14:293-300.
41. Kruschke J. K.Doing Bayesian data analysis: A tutorial with R and BUGS. Burlington, MA: Academic Press/Elsevier; 2011a:.
42. Kruschke J. K.Extrasensory perception (ESP): Bayesian estimation approach to meta-analysis [Blog post]. 2011b, August 1;:.
43. Kruschke J. K.Bayesian estimation supersedes the t test.Journal of Experimental Psychology: General. In press;:.
44. Lance C. E.More statistical and methodological myths and urban legends.Organizational Research Methods. 2011;14:279-286.
45. Leavitt K.,Mitchell T. R.,Peterson J.Theory pruning: Strategies to reduce our dense theoretical landscape.Organizational Research Methods. 2010;13:644-667.
46. Lunn D.,Spiegelhalter D.,Thomas A.,Best N.The BUGS project: Evolution, critique and future directions (with discussion).Statistics in Medicine. 2009;28:3049-3082.
47. Lunn D. J.,Thomas A.,Best N.,Spiegelhalter D.WinBUGS-A Bayesian modelling framework: Concepts, structure, and extensibility.Statistics and Computing. 2000;10:325-337.
48. Lykou A.,Ntzoufras I.WinBUGS: A tutorial.Wiley Interdisciplinary Reviews: Computational Statistics. 2011;3:385-396.
49. Maxwell S. E.The persistence of underpowered studies in psychological research: Causes, consequences, and remedies.Psychological Methods. 2004;9:147-163.
50. Maxwell S. E.,Delaney H. D.Designing experiments and analyzing data: A model comparison perspective. Mahwah, NJ: Erlbaum; 2004:.
51. McCloskey D. N.The insignificance of statistical significance.Scientific American. 1995;272:104-105.
52. Miller J.What is the probability of replicating a statistically significant effect?.Psychonomic Bulletin & Review. 2009;16:617-640.
53. Mundfrom D. J.,Perrett J. J.,Schaffer J.,Piccone A.,Roozeboom M.Bonferroni adjustments in tests for regression coefficients.Multiple Linear Regression Viewpoints. 2006;32:1-6.
54. Newman M. E. J.,Barabasi A. L.,Watts D. J.The structure and function of dynamic networks. Princeton, NJ: Princeton University Press; 2006:.
55. Nickerson R. S.Null hypothesis significance testing: A review of an old and continuing controversy.Psychological Methods. 2000;5:241-301.
56. O'Boyle E. H.,Aguinis H.The best and the rest: Revisiting the norm of normality of individual performance.Personnel Psychology. 2012;65:79-119.
57. Orlitzky M.How can significance tests be deinstitutionalized?.Organizational Research Methods. 2012;15:199-228.
58. Pierce J. R.,Aguinis H.The too-much-of-a-good-thing effect in management.Journal of Management. in press;:.
59. Plummer M.Proceedings of the 3ssrd International Workshop on Distributed Statistical Computing, March 20-22. Hornik K.Leisch F.Zeileis A., ed. Vienna, Austria: DSC; 2003:.
60. Plummer M.rjags: Bayesian graphical models using MCMC. 2011;:.
61. Poole C.Beyond the confidence interval.American Journal of Public Health. 1987;77:195-199.
62. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2012:.
63. Rogers J. L.,Howard K. I.,Vessey J. T.Using significance tests to evaluate equivalence between two experimental groups.Psychological Bulletin. 1993;113:553-565.
64. Roth P. L.,Switzer F. S.,Van Iddekinge C. H.,Oh I. S.Toward better meta-analytic input matrices: How matrix values change conclusions in human resource management simulations.Personnel Psychology. 2011;64:899-935.
65. Sasovova Z.,Mehra A.,Borgatti S. P.,Schippers M. C.Network churn: The effects of self-monitoring personality on brokerage dynamics.Administrative Science Quarterly. 2010;55:639-670.
66. Scandura T. A.,Williams E. A.Research methodology in management: Current practices, trends, and implications for future research.Academy of Management Journal. 2000;43:1248-1264.
67. Scherbaum C. A.,Ferreter J. M.Estimating statistical power and required sample sizes for organizational research using multilevel modeling.Organizational Research Methods. 2009;12:347-367.
68. Schmidt F. L.Meta-analysis: A constantly evolving research integration tool.Organizational Research Methods. 2008;11:96-113.
69. Schmidt F. L.,Raju N. S.Updating meta-analytic research findings: Bayesian approaches versus the medical model.Journal of Applied Psychology. 2007;92:297-308.
70. Schweder T.,Hjort N. L.Confidence and likelihood.Scandinavian Journal of Statistics. 2002;29:309-332.
71. Singh K.,Xie M.,Strawderman W. E.Complex datasets and inverse problems. Liu R. Y.Strawderman W. E.Zhang C.-H., ed. Beachwood, OH: Institute of Mathematical Statistics; 2007:132-150.
72. Steiger J. H.,Fouladi R. T.R2: A computer program for interval estimation, power calculations, sample size estimation, and hypothesis testing in multiple regression.Behavior Research Methods. 1992;24:581-582.
73. Sullivan K. M.,Foster D. A.Use of the confidence interval function.Epidemiology. 1990;1:39-42.
74. Thomas L.Retrospective power analysis.Conservation Biology. 1997;11:276-280.
75. Wagenmakers E. J.A practical solution to the pervasive problems of p values.Psychonomic Bulletin & Review. 2007;14:779-804.
76. Western B.,Jackman S.Bayesian inference for comparative research.American Political Science Review. 1994;88:412-423.
77. Westlake W. J.Symmetrical confidence intervals for bioequivalence trials.Biometrics. 1976;32:741-744.
78. Westlake W. J.Response to bioequivalence testing-a need to rethink.Biometrics. 1981;37:591-593.
79. Zhou J.When the presence of creative coworkers is related to creativity: Role of supervisor close monitoring, developmental feedback, and creative personality.Journal of Applied Psychology. 2003;88:413-422.